Method and apparatus for analyzing media content

ABSTRACT

Aspects of the subject disclosure may include, for example, a method for determining a first set of features in first images of first media content, generating a similarity score by processing the first set of features with a favorability model derived by identifying generative features and discriminative features of second media content that is favored by a viewer, and providing the similarity score to a network for predicting a response by the viewer to the first media content. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/861,999, filed Jan. 4, 2018, which is a continuation of U.S.application Ser. No. 14/264,183, filed Apr. 29, 2014 (now U.S. Pat. No.9,898,685), which are incorporated herein by reference in theirentirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a method and apparatus for analyzingmedia content, and more particularly for generating recommendations formedia content.

BACKGROUND

Media content is typically experienced by consumers via devices such ascomputers, televisions, radios, and mobile electronics. Media contentcan be created by many kinds of entities including traditional producersof content, such as artists, studios, and broadcasters. Today, theproliferation of video cameras, especially as integrated into mobilecommunication devices, has resulted in a large amount of contentgenerated by consumers of content. Modern communications networksprovide interconnectivity between consumers and various communicationand storage devices. As network capabilities expand, theseinterconnections provide new opportunities to enhance the ability forconsumers to enjoy media content by experiencing a variety of contentover multiple devices.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 depicts illustrative embodiments of a system that can be utilizedfor generating recommendations for media content;

FIG. 2 depicts exemplary images illustrating, in part, media contentanalyzed according to the system of FIGS. 1, 5, and 6, and the method ofFIG. 3.

FIG. 3 depicts an illustrative embodiment of a method operating inportions of the system described in FIGS. 1, 5, and 6;

FIGS. 4 and 5 depict illustrative embodiments of communication systemsfor generating recommendations for media content according toembodiments illustrated in FIGS. 1, 4, and 5;

FIG. 6 depicts an illustrative embodiment of a web portal forconfiguring a server for generating recommendations for media contentaccording to the communication systems of FIGS. 1, 5, and 6; and

FIG. 7 depicts an illustrative embodiment of a communication device; and

FIG. 8 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed, maycause the machine to perform any one or more of the methods describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for generating recommendations for media content.Newly-available media content can be compared to previously-viewed mediacontent to generate a recommendation. Images of the newly-availablemedia content can be analyzed to identify features and objects. Theseidentified features and objects can be compared to features and objectspresent in the previously-viewed media content to determine thesimilarity between the newly-available and the previously-viewed mediacontent. A recommendation can be generated for the newly-available mediacontent according to the degree of similarity or dissimilarity that hasbeen determined. Other embodiments are included in the subjectdisclosure.

One embodiment of the subject disclosure includes a device comprising aprocessor and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations,including scanning a first plurality of images of first media content todetect a first plurality of visual features present within the pluralityof images. The processor can perform operations including comparing thefirst plurality of features to a second plurality of features in asecond plurality of images of second media content to identifygenerative features and discriminative features with respect to thefirst media content and the second media content. The second mediacontent can have been previously experienced by a viewer. The processorcan further perform operations including determining a set of similaritymatrices according to the generative features and the discriminativefeatures. The processor can perform operations including processing thesimilarity matrices to generate a similarity score according tocorrelated image features of a set of previously-experienced mediacontent. The processor can perform operations including generating arecommendation for the first media content according to the similarityscore.

One embodiment of the subject disclosure includes a machine-readablestorage medium, comprising executable instructions. The executableinstructions can cause a processor to perform operations includingcomparing first features of first images of first media content tosecond features of second images of a plurality of media content itemsto identify common features and unique features with respect to thefirst media content and the plurality of media content items. Theplurality of media content items can have been experienced by a viewer.The executable instructions can also cause the processor to performoperations including generating a similarity score according to thecommon features and the unique features. The executable instructions canfurther cause the processor to perform operations including transmittingthe first media content to a device if the similarity score exceeds athreshold.

One embodiment of the subject disclosure includes a method includingdetermining, by a system comprising a processor, a first set of featuresin first images of first media content. The method can further includegenerating, by the system, a similarity score by processing the firstset of features with a favorability model derived by identifyinggenerative features and discriminative features of second media content.The second media content can be favored by a viewer. The method can alsoinclude providing, by the system, the similarity score to a network forpredicting a response by the viewer to the first media content.

FIG. 1 depicts an illustrative embodiment of a system 100 that can beutilized for automatically generating recommendations for media content.FIG. 2 depicts exemplary images illustrating, in part, automaticallygenerating recommendations for media content according to the system ofFIG. 1.

In one or more embodiments, the system 100 can include a communicationnetwork 150. The system 100 can include a subscription telecommunicationservice, such as an Internet Protocol Multimedia Subsystem (IMS) network150 for providing cellular/mobile communications, Internet access, andcontent to mobile communication devices 116A via a mobility network ofmobile base stations 117. The system can include a subscription contentservice, such as an Internet Protocol Television (IPTV) network forproviding media content to subscribers. The IPTV network can be part ofa cable, satellite, or DSL-based media content delivery system. Themedia content can be any type of viewable content, such as broadcasttelevision, cable or premium television, video on demand, orpay-per-view television. The IPTV network can deliver media content tomedia processing devices 106 and media display devices 108 at subscriberlocations via gateway devices 104. In one or more embodiments, thesystem 100 can include wireless computer devices 116B that are connectedto the communication network 150. For example, a wireless computerdevice 116B can be coupled to the communication network 150 via agateway device 104

In one or more embodiments, the system 100 can include one or morerecommendation servers 130 that are associated with the IMS network 150.In one embodiment, a recommendation server 130 can communicate withmedia content sources 165 over the IMS network 150. The recommendationserver 130 can communicate with a recommendation storage device 160. Forexample, the recommendation storage device 160 can be cloud-basedstorage, dedicated server storage, or networked storage devices, evenlocal storage devices that are linked to the network 150 via software.The recommendation server 130 can further communicate with mediaprocessor devices 106 and media display devices 108 over the IMS network150. Mobile communication devices 116A-B can communicate with the IMSnetwork 150 using one or more components of a mobility network 117, suchas cellular base stations for receiving and transmitting wirelesscommunication signals and/or wireless connections.

In one or more embodiments, a media processor device 106 can communicatewith a recommendation server 130 via the IMS Network 150 by way of agateway device 104. The media processor device 106 can receive userinputs from a remote control device for performing functions, such aspowering ON/OFF, selecting channels for viewing media programs,adjusting volume, and/or programming a digital video recorder. The mediaprocessor device 106 can receive a user input for selecting a mediaprogram and/or a channel for receiving a media program. In one example,the media processor device 106 can present an electronic programmingguide at a media device 108 for assisting in the selection of mediaprogramming. In one or more embodiments, the media processor device 106can receive cluster group images from the recommendation server 130 sothat a viewer of the media device 108 can easily review recommendationsfor media content and select and view media content.

In one or more embodiments, the recommendation server 130 can receivemedia content items from one or more media content sources. For example,media content can be uploaded from end-user sources 116A, and 116B.Uploading events can be locally directed at end-user sources 116A, and116B, or can be directed by the recommendation server 130 or by anothernetwork device. In another example, media content items can be receivedfrom one or more media sources 165, such as sources of broadcastprogramming or video-on-demand (VOD) programming. For example, a copy ofall or part of a broadcast or VOD program can be received at therecommendation server 130. In another embodiment, media content, such asvideo, audio, and/or still images can be received from a socialnetworking system or site. In one embodiment, media content items andcorresponding narrative description data can be stored at arecommendation storage device 160. The recommendation storage device 160can store the media content items, image/video feature data, similaritydata, and/or recommendation data as cloud-accessible content andinformation. The image/video feature data, similarity data, and/orrecommendation data can be generated by the recommendation server 130 orcan be received along with media content from a media content source165.

In one or more embodiments, the recommendation server 130 can trackmedia content that has been viewed by a user of the system 100. Forexample, a user can subscribe to a service for accessing media content.The recommendation service 130 access of all media content that thesubscribing user has previously-viewed. In one embodiment, therecommendation server 130 can update information trackingpreviously-viewed in recommendation storage 160 each time the subscriberviews new content via the system 100. In another embodiment, thesubscriber can provide information regarding media content consumptionby opting into an option to share this information for any mediaconsumed at devices 116A of the subscriber, regardless of the source orsystem used in accessing this content. For example, the subscriber canview content from a DVD loaded at a device 116B or can view contentdownloaded from a different system. The device 116B can shareinformation tracking these content viewings with the recommendationserver 130 so that the recommendation server 130 can maintain acomprehensive tracking database for subscriber. In another embodiment,the recommendation server 130 can obtain the viewing information for thesubscriber from another recommendation or media content service at adifferent system.

In one or more embodiments, the recommendation server 130 can receive anotice of an availability of new media content. In one embodiment,notice can be transmitted to the recommendation server 130 whenevermedia content is released for distribution over the system 100. Forexample, after theatrical release of motion pictures, it is commonpractice to release this content for video-on-demand distribution viathe Internet. Similarly, after a season of a television series, or, insome cases, a few days or weeks after a show is broadcast, content canbe released for distribution via Internet-based viewing services. Inanother example, a library of motion pictures and/or television seriesmay become available for distribution by the system 100 due to alicensing agreement. In one embodiment, the recommendation server 130can respond to a notice of new content availability by determining if asubscriber has previously-viewed the content. The recommendation serve130 can compare the newly-available content items against a listing ofpreviously-viewed content items for the subscriber from therecommendation storage 160 or another storage mechanism. In oneembodiment, if the recommendation server 130 determines that thesubscriber has not viewed the newly-available content, then therecommendation server 130 can analyze the content to determine itssimilarity to previously-viewed content and to make a viewingrecommendation accordingly.

In one or more embodiments, the recommendation server 130 can analyzemedia content to generate recommendations for content. In oneembodiment, the content can be broken down into a set of captured stillimages, or screen captures. For example, the media content can dividedinto a series of images 204, as shown in FIG. 2. In one or moreembodiments, the recommendation server 130 can analyze images from themedia content to detect images features. In one embodiment, images 204scanned from the media content can be stored separately from the mediacontent for further analysis and/or display. For example, the mediacontent and/or a series of images 204, and/or similarity scores and/orrecommendations can be stored at the recommendation storage 160, whichcan be a cloud-based resource. In one embodiment, the recommendationserver 130 can divide the media content into images 204. In anotherembodiment, image division can be performed at a different device, suchas at a user device 116B or a network device or by a service providerresource, such as the media content source 165.

In one or more embodiments, the recommendation server 130 can analyzeimages 204 from media content to determine a set of image features ofthe content. The recommendation server 130 can perform image analysisvia digital imaging techniques. The image analysis can include analyzingthe images for the presence of edges, or boundaries of features. In oneembodiment, edge detection, filtering, texture synthesis, and smoothingcan be performed to thereby detect and define feature boundaries in thevisual images. Once feature boundaries are detected in images, then therecommendation server 130 can attempt to match these two-dimensionalfeatures to known three-dimensional objects via shape analysis. Forexample, the various known three-dimensional objects can be describedaccording to three-dimensional models and/or solid geometry, which canbe translated in three-dimensions and projected onto two-dimensionspace. The two-dimensional features that are detected in the images canbe matched against a database of projected two-dimensionalrepresentations of known three-dimensional objects. In this way, therecommendation server 130 can detect the presence and orientation ofknown objects in the images. For example, a handbag 232, a sign 236,and/or human beings 220, 224, and 118 can be detected in the images 204.

In one or more embodiments, the object recognition can be used todetect, catalog, and synthesize complex objects and scene within animage. For example, the recommendation server 130 can determine that animage includes a series of rectangular objects and that these objectsare consistent with known three-dimensional models of a service counter.Further, the recommendation server 130 can determine that other objectsin the image are consistent with three-dimensional models of humanbeings. The presence of these detected objects can be recognized asconsistent with a two-dimensional representation of a scene agovernmental service center as depicted in an image 204. Thus, therecommendation server 130 can catalog the images of three people 220,224, and 228, and a government service center as a matter of the digitalimage analysis.

In one or more embodiments, the recommendation server 130 can furtherprocess the detected objects against additional recognition algorithms.For example, a facial recognition algorithm can be used to detect thepresence of human faces within the images and to compare these humanfaces to databases of known faces. The database of known faces caninclude the faces of famous persons, such as celebrities, actors, and/orother “known” people. The database can further include the faces ofpeople that are known to a user of the system 100, such as family orfriends from a social network or a digital photo book. Therecommendation server 130 can use such a database of known faces todetermine if any of the detected faces in images match faces to whichknown identities are attached. In one embodiment, where the mediacontent that is being analyzed is known to be a commercially producedmovie or television show, then this media content item can further beassociated with descriptive data, such as metadata or data from anonline database. This additional descriptive data can include, forexample, a roster of actors, who participated in the media content. Therecommendation server 130 can utilize this information to assign andcoordinate detected instances of faces of known actors with thecharacters that they are portraying in the media content piece. So, forexample, the people present in the office scene at an image 204 can bedetermined to be Actors A, B, and C, who further correspond toCharacters X, Y, and Z from the content. The recommendation server 130can use this information to further catalog features in the scene. Inone embodiment, the recommendation server 130 can include the actors'real names as well, in a catalog of features detected in the image 204.

In one or more embodiments, the recommendation server 130 can furtheranalyze the detected features and objects in the images to identifyadditional objects and/or to determine various additional information,such as the presence of animate and inanimate object, distances andorientations between detected objects, identifiable locations of scenes,multiple occurrences of objects, time of appearance and/or disappearanceof objects, colors, contrast, indoor vs. outdoor locations, orientationof objects, or condition of objects.

In one embodiment, two-dimensional features can be detected andidentified. The images 204 can be further analyzed to determine theextent to which specific two-dimensional features are visible over timein multiple images. A third dimension (time) can thereby be added as aspatio-temporal characteristic of these identified two-dimensionalfeatures to define a “three-dimensional” feature.

In one or more embodiments, the recommendation server 130 can filter theraw catalog of features detected in the media content to select the mostrelevant features. For example, the recommendation server 130 can filterbased on the number of occurrences of features such that, in one case,features that occur in less than 10% of the content are excluded fromfurther analysis. In another case, features that occur for less thanfive seconds of running time can be excluded. In another case, humanbeings that are detected in the images but not identified to knownactors or characters can be excluded. The filtering algorithm can beconfigured to limit the number of features for the sake of ease ofprocessing and analysis. In one embodiment, the filtering can beconfigured specifically for the subscriber. For example, a subscriber'suser profile can specify a particular content feature, such asautomobile chases or dancing, in which the subscriber is particularlyinterested. The recommendation server 130 can filter the media contentspecifically for these features and insure that any instances of thesefeatures are included in the set of relevant features. In one or moreembodiments, the recommendation server 130 can convert the set ofrelevant features detected in the content into matrices of features forease of manipulation in the process of comparing these features tofeatures detected in other media content.

In one or more embodiments, the recommendation server 130 can similarlyanalyze second media content for purposes of comparing the second mediacontent to the new or unviewed media content. In one embodiment, thesecond media content has been viewed by the subscriber. Therecommendation server 130 can select the second media content based onthe subscriber's media content viewing information accessed, forexample, from the recommendation storage 160. In one embodiment, thesecond media content can be selected from previously-viewed contentbased on being the last content viewed or based on being of the samegenre as the new, unviewed content or based on being content that isknown to be favorably viewed by the subscriber. In one embodiment, therecommendation server 130 can scan images from the second media content,detect objects and features, perform object recognition, filter theobjects, and generate a set of features for the second media content. Inanother embodiment, the recommendation server 130 can receive the set offeatures, as previously scanned and cataloged, for the second mediacontent from the recommendation storage device 160 or another storagelocation. A second set of features for the second media content caninclude features 244-252 that have been scanned from images 208 of thesecond media content item.

In one or more embodiments, the recommendation server 130 can comparethe first set of features from the unviewed media content to the secondset of features from the previously-viewed media content. In oneembodiment, the comparison of the first and second sets of features canbe compared using matrices of features. In one embodiment, the comparingof the first and second sets of features can result in classifyingfeatures as generative or discriminative. A generative feature can bedefined as a feature that is common or similar to both of the sets offeatures and, therefore, indicates similarity between the first andsecond media content. A discriminative feature can be defined as afeature that exists only in the first set or the second set of featuresand, therefore, indicates dissimilarity or variability between the firstand second media content.

In one or more embodiments, the recommendation server 130 can furtherprocess the analysis of similarity (and dissimilarity) into a similarityscore. The similarity score can correlate to the degree to which thefirst and second sets of features are similar. In one embodiment, therecommendation server 130 can process the first and second feature setdata, such as through a matrix comparison, into a raw results matrix ofsimilarity. The recommendation server 130 can further process the rawresults matrix according to a similarity model that has been trained forthe subscriber. In one embodiment, the similarity model can be trainedusing a training set of media content that has been previously viewed bythe subscriber. For example, the recommendation server 130 can access alist of previously viewed content that has been tracked for a subscriber(or otherwise provided by the subscriber or other services). Therecommendation server 130 can access all of the media content from thelist or can access a subset of this content. For example, therecommendation server can selectively access only those items for whichthe subscriber has indicated a favorable viewing. In another example,the recommendation server 130 can access only items for a single genreof content or for a particular time period of content creation orparticular time period of viewing (last twenty items viewed).

In one or more embodiments, the recommendation server 130 can scanimages from the second media content, detect objects and features,perform object recognition, filter the objects, and generate a set offeatures for each item of media content in the training set of mediacontent. In one embodiment, the recommendation server 130 can receiveone or more of the sets of features, as previously scanned andcataloged, for the training media content from the recommendationstorage device 160 or from another storage location. The training setsof features for the second media content can include features that havebeen scanned from images 270-282 of the training set of media contentitems 212. For example, a subscriber may have indicated a keen interestin romantic comedies.

The recommendation server 130 can provide recommendations for romanticcomedies taking into account the romantic comedies, which have beenpreviously-viewed by the subscriber. In this case, the recommendationserver 130 can train a similarity model based only on romantic comediesthat have been viewed by the subscriber. In one embodiment, all romanticcomedies are included in the analysis. If the database ofpreviously-viewed content does not include an indication as to whetherthe subscriber like the content, then the assumption in the model isthat the subscriber liked whatever he/she has viewed. In anotherembodiment, if the database includes information on the subscriber'sreaction to the content, then the model can be trained using onlyexamples of content that the viewer indicated as liking. In anotherembodiment, the model can be trained using a deep hierarchy of featuresfrom content that the subscriber like or did not like but the model canincorporate this additional information using a weighting function.

In one embodiment, the recommendation server 130 can selectively comparethe unviewed media content 204 to a single previously-viewed mediacontent item 208 or to all or part of the set of previously-viewed mediacontent items 212 or to the training set of media content or to acombination of all of these. For example, the unviewed media contentitem 204 can be compared to the subscriber's favorite movie or all timeor favorite movie in the genre or the last movie that the subscriberwatched. In this way, the recommendation server can generate asimilarity score via the model that is specific to content that thesubscriber is very familiar. In another example, the unviewed mediacontent item 204 can be compared to a large number of previously-viewedcontent items 212. In this way, the recommendation server 130 canleverage a large set of observations to reduce the chances ofmisclassifying the unviewed based on a comparison to a relatively smallset of features from a single item of previously-viewed content.

In one or more embodiments, the recommendation server 130 can furtherconvert the similarity score into a recommendation for the unviewedcontent. In one embodiment, the recommendation server 130 can simplyapply a threshold test to determine if the similarity score issufficiently high to trigger a recommendation. The threshold level canbe configured by the recommendation server 130. For example, arecommendation can be triggered liberally, even for cases of loosecorrelation, on the assumption that the subscriber does not want to missanything that could be of interest. On the other extreme, therecommendation could be triggered conservatively, only for cases ofclose correlations, on the assumption that the subscriber does not wantto waste any time watching items that have a low probability ofinterest. In one embodiment, the recommendation threshold can beconfigured according to a profile of the subscriber.

In one or more embodiments, the recommendation server 130 can providethe recommendation to devices 116A, 116B, 106 of the subscriber. Forexample, the recommendation server can transmit a notification viaemail, text, and/or other direct contact. In another example, therecommendation server 130 can provide the notice indirectly using, forexample, a recommendation channel on a television service or arecommendation section of a portal. In one embodiment, therecommendation server 130 can provide a recommendation by way ofoffering an immediate opportunity to view the content to the subscriber.For example, the recommendation server 130 embed link to access thecontent in a graphical element of a graphical user interface of aportal. In another example, the recommendation server can cause theunviewed content to be accessible at a recommendation channel of atelevision system.

FIG. 3 depicts an illustrative embodiment of a method operating in orusing portions of the system described in FIGS. 1, 4, and 5. Method 300can begin with step 304, in which images of unviewed media content canbe analyzed to determine a first set of features. The recommendationserver 130 can perform image analysis via digital imaging techniques todetect rudimentary features, such as the presence of edges, textures, orboundaries of features. The recommendation server 130 can attempt tomatch these two-dimensional features to known three-dimensional objectsvia shape analysis and can, further, perform object recognition todetect, catalog, and synthesize complex objects and scenes within animage. The recommendation server 130 can filter the raw catalog offeatures detected in the media content to select the most relevantfeatures. The recommendation server 130 can convert the set of relevantfeatures detected in the content into matrices of features for ease ofmanipulation in the process of comparing these features to featuresdetected in other media content.

At step 308, images of viewed media content can be analyzed to determinea second set of features. The recommendation server 130 can analyzesecond media content for purposes of comparing the second media contentto the new or unviewed media content. The recommendation server 130 canselect the second media content based on the subscriber's media contentviewing information accessed, for example, from the recommendationstorage 160. The recommendation server 130 can scan images from thesecond media content, detect objects and features, perform objectrecognition, filter the objects, and generate a set of features for thesecond media content. The recommendation server 130 can receive the setof features, as previously scanned and cataloged, for the second mediacontent from the recommendation storage device 160 or another storagelocation.

At step 312, the first and second sets of features can be compared toidentify generative features and discriminative features. Therecommendation server 130 can compare the first set of features from theunviewed media content to the second set of features from thepreviously-viewed media content. The comparison of the first and secondsets of features can be compared using matrices of features and canresult in classifying features as generative or discriminative.

At step 320, the generative and discriminative features can be used togenerate a similarity score according to a similarity model. This modelcan take into account the non-linear characteristics of these features,which can either be modeled using analytical manifolds, or be learntusing deep feature hierarchies derived from the data. The similarityscore can correlate to the degree to which the first and second sets offeatures are similar, by respecting the underlying non-Euclidean spacecorresponding to the metrics from which the similarity is computed. Thesimilarity score can correlate to the degree to which the first andsecond sets of features are similar. The recommendation server 130 canprocess the first and second feature set data, such as through a matrixcomparison, into a raw results matrix of similarity. The recommendationserver 130 can process the raw results matrix according to a similaritymodel that has been trained for the subscriber.

At step 324, the similarity score can be used to generate arecommendation for the unviewed content. The recommendation server 130can apply a threshold test to determine if the similarity score issufficiently high to trigger a recommendation. The recommendationthreshold can be configured according to a profile of the subscriber.The recommendation server 130 can provide the recommendation to devices116A, 116B, 106 of the subscriber. The recommendation server cantransmit a notification via email, text, and/or other direct contact.The recommendation server 130 embed link to access the content in agraphical element of a graphical user interface of a portal.

FIG. 4 depicts an illustrative embodiment of a first communicationsystem 400 for delivering media content. The communication system 400can represent an Internet Protocol Television (IPTV) media system.Communication system 400 can be overlaid or operably coupled with thesystem of FIG. 1 as another representative embodiment of communicationsystem 400. Recommendation server 130 can be utilized for automaticallygenerating recommendations for media content. Visual features andobjects detected in images of unviewed media content can be compared tovisual features and objects in images of viewed media content todetermine a degree of similarity between the viewed and unviewed mediacontent. This degree of similarity can be used to generate arecommendation for the unviewed media content.

The IPTV media system can include a super head-end office (SHO) 410 withat least one super headend office server (SHS) 411 which receives mediacontent from satellite and/or terrestrial communication systems. In thepresent context, media content can represent, for example, audiocontent, moving image content such as 2D or 3D videos, video games,virtual reality content, still image content, and combinations thereof.The SHS server 411 can forward packets associated with the media contentto one or more video head-end servers (VHS) 414 via a network of videohead-end offices (VHO) 412 according to a multicast communicationprotocol.

The VHS 414 can distribute multimedia broadcast content via an accessnetwork 418 to commercial and/or residential buildings 402 housing agateway 404 (such as a residential or commercial gateway). The accessnetwork 418 can represent a group of digital subscriber line accessmultiplexers (DSLAMs) located in a central office or a service areainterface that provide broadband services over fiber optical links orcopper twisted pairs 419 to buildings 402. The gateway 404 can usecommunication technology to distribute broadcast signals to mediaprocessors 406 such as Set-Top Boxes (STBs) which in turn presentbroadcast channels to media devices 408 such as computers or televisionsets managed in some instances by a media controller 407 (such as aninfrared or RF remote controller).

The gateway 404, the media processors 406, and media devices 408 canutilize tethered communication technologies (such as coaxial, powerlineor phone line wiring) or can operate over a wireless access protocolsuch as Wireless Fidelity (WiFi), Bluetooth, Zigbee, or other present ornext generation local or personal area wireless network technologies. Byway of these interfaces, unicast communications can also be invokedbetween the media processors 406 and subsystems of the IPTV media systemfor services such as video-on-demand (VoD), browsing an electronicprogramming guide (EPG), or other infrastructure services.

A satellite broadcast television system 429 can be used in the mediasystem of FIG. 4. The satellite broadcast television system can beoverlaid, operably coupled with, or replace the IPTV system as anotherrepresentative embodiment of communication system 400. In thisembodiment, signals transmitted by a satellite 415 that include mediacontent can be received by a satellite dish receiver 431 coupled to thebuilding 402. Modulated signals received by the satellite dish receiver431 can be transferred to the media processors 406 for demodulating,decoding, encoding, and/or distributing broadcast channels to the mediadevices 408. The media processors 406 can be equipped with a broadbandport to an Internet Service Provider (ISP) network 432 to enableinteractive services such as VoD and EPG as described above.

In yet another embodiment, an analog or digital cable broadcastdistribution system such as cable TV system 433 can be overlaid,operably coupled with, or replace the IPTV system and/or the satelliteTV system as another representative embodiment of communication system400. In this embodiment, the cable TV system 433 can also provideInternet, telephony, and interactive media services.

The subject disclosure can apply to other present or next generationover-the-air and/or landline media content services system.

Some of the network elements of the IPTV media system can be coupled toone or more computing devices 430, a portion of which can operate as aweb server for providing web portal services over the ISP network 432 towireline media devices 408 or wireless communication devices 416.

Communication system 400 can also provide for all or a portion of thecomputing devices 430 to function as a recommendation server 430. Therecommendation server 430 can use computing and communication technologyto perform function 462, which can include, among other things,automatically generating recommendations for media content from any ofseveral sources, including broadcast sources 410 and end-user devices416. The media processors 406 and wireless communication devices 416 canbe provisioned with software functions 464 and 466, respectively, toutilize the services of recommendation server 430.

Multiple forms of media services can be offered to media devices overlandline technologies such as those described above. Additionally, mediaservices can be offered to media devices by way of a wireless accessbase station 417 operating according to common wireless access protocolssuch as Global System for Mobile or GSM, Code Division Multiple Accessor CDMA, Time Division Multiple Access or TDMA, Universal MobileTelecommunications or UMTS, World interoperability for Microwave orWiMAX, Software Defined Radio or SDR, Long Term Evolution or LTE, and soon. Other present and next generation wide area wireless access networktechnologies can be used in one or more embodiments of the subjectdisclosure.

FIG. 5 depicts an illustrative embodiment of a communication system 500employing an IP Multimedia Subsystem (IMS) network architecture tofacilitate the combined services of circuit-switched systems andpacket-switched systems. Communication system 500 can be overlaid oroperably coupled with system 100 of FIG. 1 and communication system 400as another representative embodiment of communication system 400. Thesystem 500 can include a recommendation server 430 for generatingrecommendations for media content. The media content can be suppliedfrom network sources, including broadcast media sources and user devices502. The recommendation server 430 can generate and providerecommendations for media content to user devices 505 in the system viacommunications in the IMS network 550. Visual features and objectsdetected in images of unviewed media content can be compared to visualfeatures and objects in images of viewed media content to determine adegree of similarity between the viewed and unviewed media content. Thisdegree of similarity can be used to generate a recommendation for theunviewed media content.

Communication system 500 can comprise a Home Subscriber Server (HSS)540, a tElephone NUmber Mapping (ENUM) server 530, and other networkelements of an IMS network 550. The IMS network 550 can establishcommunications between IMS-compliant communication devices (CDs) 501,502, Public Switched Telephone Network (PSTN) CDs 503, 505, andcombinations thereof by way of a Media Gateway Control Function (MGCF)520 coupled to a PSTN network 560. The MGCF 520 need not be used when acommunication session involves IMS CD to IMS CD communications. Acommunication session involving at least one PSTN CD may utilize theMGCF 520.

IMS CDs 501, 502 can register with the IMS network 550 by contacting aProxy Call Session Control Function (P-CSCF) which communicates with aninterrogating CSCF (I-CSCF), which in turn, communicates with a ServingCSCF (S-CSCF) to register the CDs with the HSS 540. To initiate acommunication session between CDs, an originating IMS CD 501 can submita Session Initiation Protocol (SIP INVITE) message to an originatingP-CSCF 504 which communicates with a corresponding originating S-CSCF506. The originating S-CSCF 506 can submit the SIP INVITE message to oneor more application servers (ASs) 517 that can provide a variety ofservices to IMS subscribers.

For example, the application servers 517 can be used to performoriginating call feature treatment functions on the calling party numberreceived by the originating S-CSCF 506 in the SIP INVITE message.Originating treatment functions can include determining whether thecalling party number has international calling services, call IDblocking, calling name blocking, 7-digit dialing, and/or is requestingspecial telephony features (e.g., *72 forward calls, *73 cancel callforwarding, *67 for caller ID blocking, and so on). Based on initialfilter criteria (iFCs) in a subscriber profile associated with a CD, oneor more application servers may be invoked to provide various calloriginating feature services.

Additionally, the originating S-CSCF 506 can submit queries to the ENUMsystem 530 to translate an E.164 telephone number in the SIP INVITEmessage to a SIP Uniform Resource Identifier (URI) if the terminatingcommunication device is IMS-compliant. The SIP URI can be used by anInterrogating CSCF (I-CSCF) 507 to submit a query to the HSS 540 toidentify a terminating S-CSCF 514 associated with a terminating IMS CDsuch as reference 502. Once identified, the I-CSCF 507 can submit theSIP INVITE message to the terminating S-CSCF 514. The terminating S-CSCF514 can then identify a terminating P-CSCF 516 associated with theterminating CD 502. The P-CSCF 516 may then signal the CD 502 toestablish Voice over Internet Protocol (VoIP) communication services,thereby enabling the calling and called parties to engage in voiceand/or data communications. Based on the iFCs in the subscriber profile,one or more application servers may be invoked to provide various callterminating feature services, such as call forwarding, do not disturb,music tones, simultaneous ringing, sequential ringing, etc.

In some instances the aforementioned communication process issymmetrical. Accordingly, the terms “originating” and “terminating” inFIG. 5 may be interchangeable. It is further noted that communicationsystem 500 can be adapted to support video conferencing. In addition,communication system 500 can be adapted to provide the IMS CDs 501, 502with the multimedia and Internet services of communication system 400 ofFIG. 4.

If the terminating communication device is instead a PSTN CD such as CD503 or CD 505 (in instances where the cellular phone only supportscircuit-switched voice communications), the ENUM system 530 can respondwith an unsuccessful address resolution which can cause the originatingS-CSCF 506 to forward the call to the MGCF 520 via a Breakout GatewayControl Function (BGCF) 519. The MGCF 520 can then initiate the call tothe terminating PSTN CD over the PSTN network 560 to enable the callingand called parties to engage in voice and/or data communications.

It is further appreciated that the CDs of FIG. 5 can operate as wirelineor wireless devices. For example, the CDs of FIG. 5 can becommunicatively coupled to a cellular base station 521, a femtocell, aWiFi router, a Digital Enhanced Cordless Telecommunications (DECT) baseunit, or another suitable wireless access unit to establishcommunications with the IMS network 550 of FIG. 5. The cellular accessbase station 521 can operate according to common wireless accessprotocols such as GSM, CDMA, TDMA, UMTS, WiMax, SDR, LTE, and so on.Other present and next generation wireless network technologies can beused by one or more embodiments of the subject disclosure. Accordingly,multiple wireline and wireless communication technologies can be used bythe CDs of FIG. 5.

Cellular phones supporting LTE can support packet-switched voice andpacket-switched data communications and thus may operate asIMS-compliant mobile devices. In this embodiment, the cellular basestation 521 may communicate directly with the IMS network 550 as shownby the arrow connecting the cellular base station 521 and the P-CSCF516.

Alternative forms of a CSCF can operate in a device, system, component,or other form of centralized or distributed hardware and/or software.Indeed, a respective CSCF may be embodied as a respective CSCF systemhaving one or more computers or servers, either centralized ordistributed, where each computer or server may be configured to performor provide, in whole or in part, any method, step, or functionalitydescribed herein in accordance with a respective CSCF. Likewise, otherfunctions, servers and computers described herein, including but notlimited to, the HSS, the ENUM server, the BGCF, and the MGCF, can beembodied in a respective system having one or more computers or servers,either centralized or distributed, where each computer or server may beconfigured to perform or provide, in whole or in part, any method, step,or functionality described herein in accordance with a respectivefunction, server, or computer.

The recommendation server 430 of FIG. 4 can be operably coupled tocommunication system 500 for purposes similar to those described above.Recommendation server 430 can perform function 462 and thereby providerecommendation services to the CDs 501, 502, 503 and 505 of FIG. 5. CDs501, 502, 503 and 505, which can be adapted with software to performfunction 572 to utilize the services of the recommendation server 430.Recommendation server 430 can be an integral part of the applicationserver(s) 517 performing function 574, which can be substantiallysimilar to function 462 and adapted to the operations of the IMS network550.

For illustration purposes only, the terms S-CSCF, P-CSCF, I-CSCF, and soon, can be server devices, but may be referred to in the subjectdisclosure without the word “server.” It is also understood that anyform of a CSCF server can operate in a device, system, component, orother form of centralized or distributed hardware and software. It isfurther noted that these terms and other terms such as DIAMETER commandsare terms can include features, methodologies, and/or fields that may bedescribed in whole or in part by standards bodies such as 3^(rd)Generation Partnership Project (3GPP). It is further noted that some orall embodiments of the subject disclosure may in whole or in partmodify, supplement, or otherwise supersede final or proposed standardspublished and promulgated by 3GPP.

FIG. 6 depicts an illustrative embodiment of a web portal 602 which canbe hosted by server applications operating from the computing devices430 of the communication system 100 illustrated in FIG. 1. Communicationsystem 600 can be communicatively coupled to system 100 of FIG. 1,communication 400, and/or communication system 500. The web portal 602can be used for managing services of system 100 of FIG. 1 andcommunication systems 400-500. A web page of the web portal 602 can beaccessed by a Uniform Resource Locator (URL) with an Internet browserusing an Internet-capable communication device such as those describedin FIGS. 1, and 4-5. The web portal 602 can be configured, for example,to access a media processor 106 and services managed thereby such as aDigital Video Recorder (DVR), a Video on Demand (VoD) catalog, anElectronic Programming Guide (EPG), or a personal catalog (such aspersonal videos, pictures, audio recordings, etc.) stored at the mediaprocessor 106. The web portal 602 can also be used for provisioning IMSservices described earlier, provisioning Internet services, provisioningcellular phone services, and so on.

The web portal 602 can further be utilized to manage and provisionsoftware applications 462-466, and 572-574 to adapt these applicationsas may be desired by subscribers and/or service providers of system 100of FIG. 1, and 400-500 of FIGS. 4-5.

FIG. 7 depicts an illustrative embodiment of a communication device 700.Communication device 700 can serve in whole or in part as anillustrative embodiment of the devices depicted in FIG. 1 and FIGS. 4-5.Communication device 700 in whole or in part can represent any of thecommunication devices described in FIGS. 1 and 4-5 and can be configuredto perform portions of method 300 of FIG. 3.

Communication device 700 can comprise a wireline and/or wirelesstransceiver 702 (herein transceiver 702), a user interface (UI) 704, apower supply 714, a location receiver 716, a motion sensor 718, anorientation sensor 720, and a controller 706 for managing operationsthereof. The transceiver 702 can support short-range or long-rangewireless access technologies such as Bluetooth, ZigBee, WiFi, DECT, orcellular communication technologies, just to mention a few. Cellulartechnologies can include, for example, CDMA-1×, UMTS/HSDPA, GSM/GPRS,TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generationwireless communication technologies as they arise. The transceiver 702can also be adapted to support circuit-switched wireline accesstechnologies (such as PSTN), packet-switched wireline accesstechnologies (such as TCP/IP, VoIP, etc.), and combinations thereof.

The UI 704 can include a depressible or touch-sensitive keypad 708 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device700. The keypad 708 can be an integral part of a housing assembly of thecommunication device 700 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth. The keypad 708 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 704 can further include a display710 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 700. In anembodiment where the display 710 is touch-sensitive, a portion or all ofthe keypad 708 can be presented by way of the display 710 withnavigation features.

The display 710 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 700 can be adapted to present a user interface withgraphical user interface (GUI) elements that can be selected by a userwith a touch of a finger. The touch screen display 710 can be equippedwith capacitive, resistive or other forms of sensing technology todetect how much surface area of a user's finger has been placed on aportion of the touch screen display. This sensing information can beused to control the manipulation of the GUI elements or other functionsof the user interface. The display 710 can be an integral part of thehousing assembly of the communication device 700 or an independentdevice communicatively coupled thereto by a tethered wireline interface(such as a cable) or a wireless interface.

The UI 704 can also include an audio system 712 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 712 can further include amicrophone for receiving audible signals of an end user. The audiosystem 712 can also be used for voice recognition applications. The UI704 can further include an image sensor 713 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 714 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 700 to facilitatelong-range or short-range portable applications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 716 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 700 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 718can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 700 in three-dimensional space. Theorientation sensor 720 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device700 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 700 can use the transceiver 702 to alsodetermine a proximity to a cellular, WiFi, Bluetooth, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 706 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 400.

Other components not shown in FIG. 7 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 700 can include a reset button (not shown). The reset button canbe used to reset the controller 706 of the communication device 700. Inyet another embodiment, the communication device 700 can also include afactory default setting button positioned, for example, below a smallhole in a housing assembly of the communication device 700 to force thecommunication device 700 to re-establish factory settings. In thisembodiment, a user can use a protruding object such as a pen or paperclip tip to reach into the hole and depress the default setting button.The communication device 700 can also include a slot for adding orremoving an identity module such as a Subscriber Identity Module (SIM)card. SIM cards can be used for identifying subscriber services,executing programs, storing subscriber data, and so forth.

The communication device 700 as described herein can operate with moreor less of the circuit components shown in FIG. 7. These variantembodiments can be used in one or more embodiments of the subjectdisclosure.

The communication device 700 can be adapted to perform the functions ofdevices of FIG. 1, the media processor 406, the media devices 408, orthe portable communication devices 416 of FIG. 4, as well as the IMS CDs501-502 and PSTN CDs 503-505 of FIG. 5. It will be appreciated that thecommunication device 700 can also represent other devices that canoperate in the system of FIG. 1, and the communication systems 400-500of FIGS. 4-5, such as a gaming console and a media player.

The communication device 700 shown in FIG. 7 or portions thereof canserve as a representation of one or more of the devices of system 100 ofFIG. 1, communication system 400, and communication system 500. Inaddition, the controller 706 can be adapted in various embodiments toperform the functions 462-466 and 572-574, respectively.

Upon reviewing the aforementioned embodiments, it would be evident to anartisan with ordinary skill in the art that said embodiments can bemodified, reduced, or enhanced without departing from the scope of theclaims described below. For example, the recommendation server 130 canprovide “anti-recommendation” information to the subscriber. Theanti-recommendation information can describe media content that therecommendation server 130 has determined to not fit the interests of thesubscriber. This information can be used by the subscribe to provide areality check on the recommendations and/or to provide a list of mediacontent that could be viewed to expand the subscriber's viewing tastes.

In one or more embodiments, the recommendation server 130 can providenot only the recommendation but also the similarity score. Thesubscriber can use the additional information to see how similar the newcontent is to the old content. In another embodiment, the recommendationserver 130 can provide degrees of recommendations based on thesimilarity score. For example, a high similarity score could be used togenerate a “strong” or “two thumbs up” recommendation. Alternatively, asimilarity score that just barely clears the recommendation thresholdcan be used to generate a “weak” or “one thumbs up” recommendation.

In one or more embodiments, the recommendation server 130 can performthe analysis on a group of unviewed content. The recommendation can beput in the form of a ranked list showing strong to weak recommendations.In another recommendation, the recommendation server 130 can providerelative recommendations where, for example, the unviewed media contentis placed into a list of all of the previously-viewed media content at aposition relative to how much the subscriber is predicted to like thecontent. In another embodiment, the recommendation can be listedaccording to its relative score against all other content recommendedover a time period, such as for the current year.

In one or more embodiments, the recommendation server 130 can performthe analysis for the unviewed media content based on a promotionaltrailer or a set of still images that have been provided for the contentin pre-distribution phase. The recommendation can be labeled as“preliminary” or “trailer-based.” In another embodiment, therecommendation server 130 can provide a recommendation that can savebandwidth needed for transmitting an entire video to a subscriber that,in reality, the subscriber will not have any interest in viewing. Inanother embodiment, the recommendation server 130 can provide arecommendation that can save the time of the subscriber by avoidingviewing content that will be of no interest. In another embodiment, therecommendation can be in the form of a priority list of content items.

In one or more embodiments, the recommendation server 130 can detectclosed-captioning information and/or displayed text in the images. Therecommendation server 130 can compare the text present in the unviewedand viewed content as part of the similarity analysis. In one or moreembodiments, the unviewed content can be compared to more than one genreof previously-viewed content. Similarity scores and/or recommendationscan be generated for the unviewed content with respect to each genre.

In one or more embodiments, the similarity score and/or recommendationcan be provided to mobility devices and/or to websites, to provideinformation for streaming content and/or renting physical media (DVDs)and/or subscribing to services. In one or more embodiments, thesubscriber can have a trained model that is based on his/herpreviously-viewed videos. The model can be used in any number ofapplications and can be transferable between applications and/or contentservice providers. In one or more embodiments, the notion of similaritycan be used for recommending products and/or services based on mediacontent associated with those products and services. For example, a newmedia content (an advertisement) describing a product or service can beanalyzed with respect to prior content describing this product orservice. The new media content can be directed to the subscriber basedon knowledge of the similarity analysis.

In one or more embodiments, the similarity model can be trained byextracting, for example, 20 features from each content item in atraining set of 10 content items and, then, determining which of these200-features is common to two or more of the videos and which of areunique. Next, vectors can be used to compute distances between each ofthe videos using the vectors. In one or more embodiments, deep learningmodels can be used to automatically learn optimal features and comparethe features to the model.

FIG. 8 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 800 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as the recommendation server 430, the mediaprocessor 406, the recommendation storage device 160, the mobilecommunication device 116A, and the computing device 116B of FIGS. 1-5.In some embodiments, the machine may be connected (e.g., using a network826) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server or a client user machine inserver-client user network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet PC, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

The computer system 800 may include a processor (or controller) 802(e.g., a central processing unit (CPU)), a graphics processing unit(GPU, or both), a main memory 804 and a static memory 806, whichcommunicate with each other via a bus 808. The computer system 800 mayfurther include a display unit 810 (e.g., a liquid crystal display(LCD), a flat panel, or a solid state display). The computer system 800may include an input device 812 (e.g., a keyboard), a cursor controldevice 814 (e.g., a mouse), a disk drive unit 816, a signal generationdevice 818 (e.g., a speaker or remote control) and a network interfacedevice 820. In distributed environments, the embodiments described inthe subject disclosure can be adapted to utilize multiple display units810 controlled by two or more computer systems 800. In thisconfiguration, presentations described by the subject disclosure may inpart be shown in a first of the display units 810, while the remainingportion is presented in a second of the display units 810.

The disk drive unit 816 may include a tangible computer-readable storagemedium 822 on which is stored one or more sets of instructions (e.g.,software 824) embodying any one or more of the methods or functionsdescribed herein, including those methods illustrated above. Theinstructions 824 may also reside, completely or at least partially,within the main memory 804, the static memory 806, and/or within theprocessor 802 during execution thereof by the computer system 800. Themain memory 804 and the processor 802 also may constitute tangiblecomputer-readable storage media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Application specific integrated circuits andprogrammable logic array can use downloadable instructions for executingstate machines and/or circuit configurations to implement embodiments ofthe subject disclosure. Applications that may include the apparatus andsystems of various embodiments broadly include a variety of electronicand computer systems. Some embodiments implement functions in two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals communicated between and through the modules,or as portions of an application-specific integrated circuit. Thus, theexample system is applicable to software, firmware, and hardwareimplementations.

In accordance with various embodiments of the subject disclosure, theoperations or methods described herein are intended for operation assoftware programs or instructions running on or executed by a computerprocessor or other computing device, and which may include other formsof instructions manifested as a state machine implemented with logiccomponents in an application specific integrated circuit or fieldprogrammable gate array. Furthermore, software implementations (e.g.,software programs, instructions, etc.) including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein. It is furthernoted that a computing device such as a processor, a controller, a statemachine or other suitable device for executing instructions to performoperations or methods may perform such operations directly or indirectlyby way of one or more intermediate devices directed by the computingdevice.

While the tangible computer-readable storage medium 822 is shown in anexample embodiment to be a single medium, the term “tangiblecomputer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “tangible computer-readable storage medium” shallalso be taken to include any non-transitory medium that is capable ofstoring or encoding a set of instructions for execution by the machineand that cause the machine to perform any one or more of the methods ofthe subject disclosure. The term “non-transitory” as in a non-transitorycomputer-readable storage includes without limitation memories, drives,devices and anything tangible but not a signal per se.

The term “tangible computer-readable storage medium” shall accordinglybe taken to include, but not be limited to: solid-state memories such asa memory card or other package that houses one or more read-only(non-volatile) memories, random access memories, or other re-writable(volatile) memories, a magneto-optical or optical medium such as a diskor tape, or other tangible media which can be used to store information.Accordingly, the disclosure is considered to include any one or more ofa tangible computer-readable storage medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are from time-to-timesuperseded by faster or more efficient equivalents having essentiallythe same functions. Wireless standards for device detection (e.g.,RFID), short-range communications (e.g., Bluetooth, WiFi, Zigbee), andlong-range communications (e.g., WiMAX, GSM, CDMA, LTE) can be used bycomputer system 800.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Theexemplary embodiments can include combinations of features and/or stepsfrom multiple embodiments. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. Figuresare also merely representational and may not be drawn to scale. Certainproportions thereof may be exaggerated, while others may be minimized.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,can be used in the subject disclosure. In one or more embodiments,features that are positively recited can also be excluded from theembodiment with or without replacement by another component or step. Thesteps or functions described with respect to the exemplary processes ormethods can be performed in any order. The steps or functions describedwith respect to the exemplary processes or methods can be performedalone or in combination with other steps or functions (from otherembodiments or from other steps that have not been described).

Less than all of the steps or functions described with respect to theexemplary processes or methods can also be performed in one or more ofthe exemplary embodiments. Further, the use of numerical terms todescribe a device, component, step or function, such as first, second,third, and so forth, is not intended to describe an order or functionunless expressly stated so. The use of the terms first, second, thirdand so forth, is generally to distinguish between devices, components,steps or functions unless expressly stated otherwise. Additionally, oneor more devices or components described with respect to the exemplaryembodiments can facilitate one or more functions, where the facilitating(e.g., facilitating access or facilitating establishing a connection)can include less than every step needed to perform the function or caninclude all of the steps needed to perform the function.

In one or more embodiments, a processor (which can include a controlleror circuit) has been described that performs various functions. Itshould be understood that the processor can be multiple processors,which can include distributed processors or parallel processors in asingle machine or multiple machines. The processor can be used insupporting a virtual processing environment. The virtual processingenvironment may support one or more virtual machines representingcomputers, servers, or other computing devices. In such virtualmachines, components such as microprocessors and storage devices may bevirtualized or logically represented. The processor can include a statemachine, application specific integrated circuit, and/or programmablegate array including a Field PGA. In one or more embodiments, when aprocessor executes instructions to perform “operations”, this caninclude the processor performing the operations directly and/orfacilitating, directing, or cooperating with another device or componentto perform the operations.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, it can beseen that various features are grouped together in a single embodimentfor the purpose of streamlining the disclosure. This method ofdisclosure is not to be interpreted as reflecting an intention that theclaimed embodiments require more features than are expressly recited ineach claim. Rather, as the following claims reflect, inventive subjectmatter lies in less than all features of a single disclosed embodiment.Thus the following claims are hereby incorporated into the DetailedDescription, with each claim standing on its own as a separately claimedsubject matter.

What is claimed is:
 1. A device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, comprising: generating a first set of image objects by filtering image objects scanned from first media content using first criteria, wherein the first criteria comprise a number of occurrences of an image object, a running time of occurrence of the image object, or a combination thereof; scanning a plurality of training images of a plurality of training media content items associated with a viewer to detect a plurality of training image objects; identifying a plurality of training visual features of the plurality of training image objects; generating a similarity model according to the plurality of training visual features; generating a similarity score by processing the first set of image objects with the similarity model; and providing the similarity score for predicting a response from equipment of the viewer regarding the first media content.
 2. The device of claim 1, wherein the operations further comprise: accessing a plurality of favorability ratings for the plurality of training media content items; and weighting the similarity score according to the favorability ratings.
 3. The device of claim 1, wherein generating the similarity model further comprises: comparing training visual features of the plurality of training visual features to identify a plurality of training generative visual features and a plurality of training discriminative visual features with respect to combinations of the training media content items; and generating a model of correlated image objects according to the plurality of training generative visual features and the plurality of training discriminative visual features.
 4. The device of claim 1, wherein the operations further comprise generating a recommendation for the first media content according to the similarity score.
 5. The device of claim 4, wherein the recommendation is generated in accordance with the similarity score exceeding a threshold.
 6. The device of claim 1, wherein the operations further comprise training the similarity model using a hierarchy of features of the plurality of training media content items.
 7. The device of claim 6, wherein the similarity model is trained using only examples of content for which the viewer has indicated a favorable viewing.
 8. The device of claim 1, wherein the plurality of training media content items comprise a predetermined number of previously viewed media content items.
 9. The device of claim 1, wherein the training media content items are selected from a set of content being viewed by the viewer within a time period.
 10. The device of claim 1, wherein the first criteria include information from a viewer profile.
 11. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, comprising: generating a first set of image objects by filtering image objects scanned from first media content using first criteria, wherein the first criteria comprise a number of occurrences of an image object, a running time of occurrence of the image object, or a combination thereof; scanning a plurality of training images of a plurality of training media content items associated with a viewer to detect a plurality of training image objects; identifying a plurality of training visual features of the plurality of training image objects; generating a similarity model according to the plurality of training visual features; generating a similarity score by processing the first set of image objects with the similarity model; and generating a recommendation for the first media content according to the similarity score.
 12. The non-transitory machine-readable medium of claim 11, wherein the recommendation is generated in accordance with the similarity score exceeding a threshold.
 13. The non-transitory machine-readable medium of claim 11, wherein the operations further comprise providing the similarity score for predicting a response from equipment of the viewer regarding the first media content.
 14. The non-transitory machine-readable medium of claim 11, wherein generating the similarity model further comprises: comparing training visual features of the plurality of training visual features to identify a plurality of training generative visual features and a plurality of training discriminative visual features with respect to combinations of the training media content items; and generating a model of correlated image objects according to the plurality of training generative visual features and the plurality of training discriminative visual features.
 15. The non-transitory machine-readable medium of claim 11, wherein the operations further comprise training the similarity model using a hierarchy of features of the plurality of training media content items.
 16. A method comprising: generating, by a processing system including a processor, a first set of image objects by filtering image objects scanned from first media content using first criteria, wherein the first criteria comprise a number of occurrences of an image object, a running time of occurrence of the image object, or a combination thereof; scanning, by the processing system, a plurality of training images of a plurality of training media content items associated with a viewer to detect a plurality of training image objects; identifying, by the processing system, a plurality of training visual features of the plurality of training image objects; generating, by the processing system, a similarity model according to the plurality of training visual features; and generating, by the processing system, a similarity score by processing the first set of image objects with the similarity model.
 17. The method of claim 16, further comprising: providing, by the processing system, the similarity score for predicting a response from equipment of the viewer regarding the first media content; and generating, by the processing system, a recommendation for the first media content according to the similarity score.
 18. The method of claim 16, wherein generating the similarity model further comprises comparing training visual features of the plurality of training visual features to identify a plurality of training generative visual features and a plurality of training discriminative visual features with respect to combinations of the training media content items.
 19. The method of claim 16, further comprising training, by the processing system, the similarity model using a hierarchy of features of the plurality of training media content items.
 20. The method of claim 16, wherein the plurality of training media content items comprise a predetermined number of previously viewed media content items. 